from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 52.750785 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 4.822835 |
| KNeighborsClassifier_kd_tree | 0.0 | 6.0 | 15.298456 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 39.911819 |
| KMeans_tall | 0.0 | 1.0 | 40.039003 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 20.569132 |
| KMeans_short | 0.0 | 0.0 | 16.024887 |
| daal4py_KMeans_short | 0.0 | 0.0 | 8.350271 |
| LogisticRegression | 0.0 | 1.0 | 2.472604 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 55.306440 |
| Ridge | 0.0 | 0.0 | 24.980500 |
| daal4py_Ridge | 0.0 | 0.0 | 6.617601 |
| total | 0.0 | 30.0 | 47.211386 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.137 | 0.002 | 1000000 | 1000000 | 100 | -1 | 1 | 5.830 | NaN | 0.988 | 0.982 | 0.498 | 0.002 | 0.276 | 0.003 | See |
| 1 | KNeighborsClassifier | predict | 25.871 | 0.260 | 1000000 | 1000 | 100 | -1 | 1 | 0.000 | 0.026 | 0.988 | 0.982 | 1.749 | 0.085 | 14.789 | 0.732 | See |
| 2 | KNeighborsClassifier | predict | 0.192 | 0.019 | 1000000 | 1 | 100 | -1 | 1 | 0.004 | 0.000 | 0.988 | 0.982 | 0.095 | 0.001 | 2.021 | 0.203 | See |
| 3 | KNeighborsClassifier | fit | 0.146 | 0.003 | 1000000 | 1000000 | 100 | -1 | 5 | 5.476 | NaN | 0.988 | 0.982 | 0.502 | 0.004 | 0.291 | 0.007 | See |
| 4 | KNeighborsClassifier | predict | 34.755 | 0.000 | 1000000 | 1000 | 100 | -1 | 5 | 0.000 | 0.035 | 0.988 | 0.982 | 1.727 | 0.012 | 20.130 | 0.144 | See |
| 5 | KNeighborsClassifier | predict | 0.178 | 0.007 | 1000000 | 1 | 100 | -1 | 5 | 0.004 | 0.000 | 0.988 | 0.982 | 0.093 | 0.001 | 1.909 | 0.078 | See |
| 6 | KNeighborsClassifier | fit | 0.137 | 0.001 | 1000000 | 1000000 | 100 | -1 | 100 | 5.851 | NaN | 0.988 | 0.982 | 0.496 | 0.002 | 0.276 | 0.002 | See |
| 7 | KNeighborsClassifier | predict | 34.711 | 0.000 | 1000000 | 1000 | 100 | -1 | 100 | 0.000 | 0.035 | 0.988 | 0.982 | 1.801 | 0.019 | 19.277 | 0.203 | See |
| 8 | KNeighborsClassifier | predict | 0.192 | 0.016 | 1000000 | 1 | 100 | -1 | 100 | 0.004 | 0.000 | 0.988 | 0.982 | 0.094 | 0.001 | 2.039 | 0.168 | See |
| 9 | KNeighborsClassifier | fit | 0.145 | 0.003 | 1000000 | 1000000 | 100 | 1 | 1 | 5.509 | NaN | 0.988 | 0.982 | 0.509 | 0.007 | 0.286 | 0.007 | See |
| 10 | KNeighborsClassifier | predict | 13.364 | 0.010 | 1000000 | 1000 | 100 | 1 | 1 | 0.000 | 0.013 | 0.988 | 0.982 | 1.715 | 0.010 | 7.790 | 0.044 | See |
| 11 | KNeighborsClassifier | predict | 0.204 | 0.002 | 1000000 | 1 | 100 | 1 | 1 | 0.004 | 0.000 | 0.988 | 0.982 | 0.093 | 0.001 | 2.208 | 0.026 | See |
| 12 | KNeighborsClassifier | fit | 0.135 | 0.001 | 1000000 | 1000000 | 100 | 1 | 5 | 5.932 | NaN | 0.988 | 0.982 | 0.496 | 0.004 | 0.272 | 0.002 | See |
| 13 | KNeighborsClassifier | predict | 22.901 | 0.004 | 1000000 | 1000 | 100 | 1 | 5 | 0.000 | 0.023 | 0.988 | 0.982 | 1.714 | 0.006 | 13.359 | 0.049 | See |
| 14 | KNeighborsClassifier | predict | 0.215 | 0.001 | 1000000 | 1 | 100 | 1 | 5 | 0.004 | 0.000 | 0.988 | 0.982 | 0.092 | 0.001 | 2.338 | 0.025 | See |
| 15 | KNeighborsClassifier | fit | 0.136 | 0.001 | 1000000 | 1000000 | 100 | 1 | 100 | 5.889 | NaN | 0.988 | 0.982 | 0.497 | 0.003 | 0.273 | 0.002 | See |
| 16 | KNeighborsClassifier | predict | 22.916 | 0.006 | 1000000 | 1000 | 100 | 1 | 100 | 0.000 | 0.023 | 0.988 | 0.982 | 1.794 | 0.014 | 12.772 | 0.101 | See |
| 17 | KNeighborsClassifier | predict | 0.215 | 0.002 | 1000000 | 1 | 100 | 1 | 100 | 0.004 | 0.000 | 0.988 | 0.982 | 0.093 | 0.001 | 2.309 | 0.025 | See |
| 18 | KNeighborsClassifier | fit | 0.055 | 0.000 | 1000000 | 1000000 | 2 | -1 | 1 | 0.290 | NaN | 0.988 | 0.982 | 0.097 | 0.003 | 0.567 | 0.017 | See |
| 19 | KNeighborsClassifier | predict | 22.873 | 0.068 | 1000000 | 1000 | 2 | -1 | 1 | 0.000 | 0.023 | 0.988 | 0.982 | 0.258 | 0.001 | 88.682 | 0.325 | See |
| 20 | KNeighborsClassifier | predict | 0.020 | 0.001 | 1000000 | 1 | 2 | -1 | 1 | 0.001 | 0.000 | 0.988 | 0.982 | 0.005 | 0.000 | 3.821 | 0.344 | See |
| 21 | KNeighborsClassifier | fit | 0.056 | 0.000 | 1000000 | 1000000 | 2 | -1 | 5 | 0.286 | NaN | 0.988 | 0.982 | 0.098 | 0.003 | 0.572 | 0.016 | See |
| 22 | KNeighborsClassifier | predict | 33.329 | 0.000 | 1000000 | 1000 | 2 | -1 | 5 | 0.000 | 0.033 | 0.988 | 0.982 | 0.260 | 0.002 | 128.313 | 0.912 | See |
| 23 | KNeighborsClassifier | predict | 0.035 | 0.002 | 1000000 | 1 | 2 | -1 | 5 | 0.000 | 0.000 | 0.988 | 0.982 | 0.005 | 0.001 | 6.678 | 0.731 | See |
| 24 | KNeighborsClassifier | fit | 0.058 | 0.001 | 1000000 | 1000000 | 2 | -1 | 100 | 0.278 | NaN | 0.988 | 0.982 | 0.097 | 0.002 | 0.594 | 0.012 | See |
| 25 | KNeighborsClassifier | predict | 33.537 | 0.000 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.034 | 0.988 | 0.982 | 0.307 | 0.002 | 109.392 | 0.559 | See |
| 26 | KNeighborsClassifier | predict | 0.036 | 0.002 | 1000000 | 1 | 2 | -1 | 100 | 0.000 | 0.000 | 0.988 | 0.982 | 0.006 | 0.000 | 6.294 | 0.640 | See |
| 27 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | 1 | 1 | 0.280 | NaN | 0.988 | 0.982 | 0.096 | 0.000 | 0.594 | 0.006 | See |
| 28 | KNeighborsClassifier | predict | 10.922 | 0.020 | 1000000 | 1000 | 2 | 1 | 1 | 0.000 | 0.011 | 0.988 | 0.982 | 0.259 | 0.001 | 42.181 | 0.229 | See |
| 29 | KNeighborsClassifier | predict | 0.016 | 0.001 | 1000000 | 1 | 2 | 1 | 1 | 0.001 | 0.000 | 0.988 | 0.982 | 0.005 | 0.000 | 2.958 | 0.258 | See |
| 30 | KNeighborsClassifier | fit | 0.058 | 0.001 | 1000000 | 1000000 | 2 | 1 | 5 | 0.278 | NaN | 0.988 | 0.982 | 0.097 | 0.001 | 0.593 | 0.008 | See |
| 31 | KNeighborsClassifier | predict | 21.079 | 0.067 | 1000000 | 1000 | 2 | 1 | 5 | 0.000 | 0.021 | 0.988 | 0.982 | 0.260 | 0.001 | 81.174 | 0.468 | See |
| 32 | KNeighborsClassifier | predict | 0.029 | 0.001 | 1000000 | 1 | 2 | 1 | 5 | 0.001 | 0.000 | 0.988 | 0.982 | 0.005 | 0.000 | 5.348 | 0.489 | See |
| 33 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | 1 | 100 | 0.280 | NaN | 0.988 | 0.982 | 0.097 | 0.003 | 0.590 | 0.020 | See |
| 34 | KNeighborsClassifier | predict | 20.968 | 0.009 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.021 | 0.988 | 0.982 | 0.308 | 0.002 | 68.071 | 0.528 | See |
| 35 | KNeighborsClassifier | predict | 0.029 | 0.001 | 1000000 | 1 | 2 | 1 | 100 | 0.001 | 0.000 | 0.988 | 0.982 | 0.006 | 0.000 | 5.208 | 0.397 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 3.136 | 0.048 | 1000000 | 1000000 | 10 | -1 | 1 | 0.026 | NaN | 0.986 | 0.985 | 0.684 | 0.007 | 4.587 | 0.084 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.454 | 0.005 | 1000000 | 1000 | 10 | -1 | 1 | 0.000 | 0.000 | 0.986 | 0.985 | 0.106 | 0.001 | 4.264 | 0.056 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.005 | 1000000 | 1 | 10 | -1 | 1 | 0.015 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 30.089 | 32.359 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 3.129 | 0.052 | 1000000 | 1000000 | 10 | -1 | 5 | 0.026 | NaN | 0.986 | 0.985 | 0.717 | 0.015 | 4.366 | 0.118 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.833 | 0.012 | 1000000 | 1000 | 10 | -1 | 5 | 0.000 | 0.001 | 0.986 | 0.985 | 0.196 | 0.002 | 4.244 | 0.076 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 10 | -1 | 5 | 0.031 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 7.047 | 3.744 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 3.099 | 0.055 | 1000000 | 1000000 | 10 | -1 | 100 | 0.026 | NaN | 0.986 | 0.985 | 0.691 | 0.005 | 4.484 | 0.085 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.605 | 0.026 | 1000000 | 1000 | 10 | -1 | 100 | 0.000 | 0.003 | 0.986 | 0.985 | 0.592 | 0.003 | 4.404 | 0.051 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | -1 | 100 | 0.023 | 0.000 | 0.986 | 0.985 | 0.001 | 0.000 | 4.590 | 2.550 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.087 | 0.038 | 1000000 | 1000000 | 10 | 1 | 1 | 0.026 | NaN | 0.986 | 0.985 | 0.714 | 0.005 | 4.321 | 0.061 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.779 | 0.005 | 1000000 | 1000 | 10 | 1 | 1 | 0.000 | 0.001 | 0.986 | 0.985 | 0.109 | 0.002 | 7.160 | 0.133 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | 1 | 1 | 0.092 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 4.604 | 3.234 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.118 | 0.061 | 1000000 | 1000000 | 10 | 1 | 5 | 0.026 | NaN | 0.986 | 0.985 | 0.694 | 0.007 | 4.491 | 0.098 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.467 | 0.008 | 1000000 | 1000 | 10 | 1 | 5 | 0.000 | 0.001 | 0.986 | 0.985 | 0.197 | 0.003 | 7.461 | 0.113 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | 1 | 5 | 0.075 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 3.033 | 1.710 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.106 | 0.041 | 1000000 | 1000000 | 10 | 1 | 100 | 0.026 | NaN | 0.986 | 0.985 | 0.720 | 0.017 | 4.314 | 0.115 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 5.055 | 0.056 | 1000000 | 1000 | 10 | 1 | 100 | 0.000 | 0.005 | 0.986 | 0.985 | 0.596 | 0.007 | 8.489 | 0.136 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.001 | 1000000 | 1 | 10 | 1 | 100 | 0.041 | 0.000 | 0.986 | 0.985 | 0.001 | 0.000 | 2.602 | 1.276 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 0.780 | 0.011 | 1000000 | 1000000 | 2 | -1 | 1 | 0.021 | NaN | 0.986 | 0.985 | 0.443 | 0.005 | 1.762 | 0.031 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.024 | 0.001 | 1000000 | 1000 | 2 | -1 | 1 | 0.001 | 0.000 | 0.986 | 0.985 | 0.001 | 0.000 | 33.383 | 12.665 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.010 | 0.024 | 1000000 | 1 | 2 | -1 | 1 | 0.002 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 89.822 | 223.502 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 0.746 | 0.014 | 1000000 | 1000000 | 2 | -1 | 5 | 0.021 | NaN | 0.986 | 0.985 | 0.446 | 0.008 | 1.671 | 0.043 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.026 | 0.001 | 1000000 | 1000 | 2 | -1 | 5 | 0.001 | 0.000 | 0.986 | 0.985 | 0.001 | 0.000 | 22.794 | 6.480 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | -1 | 5 | 0.008 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 19.713 | 17.380 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 0.747 | 0.006 | 1000000 | 1000000 | 2 | -1 | 100 | 0.021 | NaN | 0.986 | 0.985 | 0.443 | 0.005 | 1.686 | 0.024 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.044 | 0.001 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.000 | 0.986 | 0.985 | 0.006 | 0.001 | 6.940 | 0.789 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | -1 | 100 | 0.008 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 16.832 | 12.783 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 0.759 | 0.005 | 1000000 | 1000000 | 2 | 1 | 1 | 0.021 | NaN | 0.986 | 0.985 | 0.444 | 0.005 | 1.709 | 0.024 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.023 | 0.001 | 1000000 | 1000 | 2 | 1 | 1 | 0.001 | 0.000 | 0.986 | 0.985 | 0.001 | 0.000 | 30.226 | 12.259 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 1 | 0.026 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 5.395 | 4.587 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 0.760 | 0.011 | 1000000 | 1000000 | 2 | 1 | 5 | 0.021 | NaN | 0.986 | 0.985 | 0.445 | 0.005 | 1.709 | 0.030 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.024 | 0.001 | 1000000 | 1000 | 2 | 1 | 5 | 0.001 | 0.000 | 0.986 | 0.985 | 0.001 | 0.000 | 21.826 | 6.525 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 5 | 0.025 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 5.409 | 4.516 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 0.745 | 0.008 | 1000000 | 1000000 | 2 | 1 | 100 | 0.021 | NaN | 0.986 | 0.985 | 0.441 | 0.005 | 1.690 | 0.026 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.053 | 0.001 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.000 | 0.986 | 0.985 | 0.006 | 0.001 | 8.466 | 0.979 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 100 | 0.024 | 0.000 | 0.986 | 0.985 | 0.000 | 0.000 | 4.315 | 3.232 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.558 | 0.005 | 1000000 | 1000000 | 2 | k-means++ | 30 | 0.861 | NaN | 0.001 | 30 | 0.002 | 0.406 | 0.019 | 1.375 | 0.066 | See |
| 1 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 2 | k-means++ | 30 | 0.013 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 8.945 | 6.826 | See |
| 2 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | k-means++ | 30 | 0.013 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 10.289 | 8.504 | See |
| 3 | KMeans_tall | fit | 0.482 | 0.002 | 1000000 | 1000000 | 2 | random | 30 | 0.995 | NaN | 0.001 | 30 | 0.002 | 0.366 | 0.013 | 1.317 | 0.048 | See |
| 4 | KMeans_tall | predict | 0.001 | 0.001 | 1000000 | 1000 | 2 | random | 30 | 0.011 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 9.379 | 7.514 | See |
| 5 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | random | 30 | 0.013 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 10.282 | 8.101 | See |
| 6 | KMeans_tall | fit | 6.138 | 0.142 | 1000000 | 1000000 | 100 | k-means++ | 30 | 3.910 | NaN | 0.001 | 30 | 0.002 | 3.162 | 0.029 | 1.941 | 0.049 | See |
| 7 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | k-means++ | 30 | 0.519 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 5.291 | 3.035 | See |
| 8 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 100 | k-means++ | 30 | 0.618 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 10.452 | 8.210 | See |
| 9 | KMeans_tall | fit | 5.749 | 0.022 | 1000000 | 1000000 | 100 | random | 30 | 4.174 | NaN | 0.001 | 30 | 0.002 | 3.185 | 0.064 | 1.805 | 0.037 | See |
| 10 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | random | 30 | 0.520 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 5.051 | 3.644 | See |
| 11 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 100 | random | 30 | 0.629 | 0.0 | 0.001 | 30 | 0.002 | 0.000 | 0.000 | 8.357 | 5.932 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.242 | 0.012 | 10000 | 10000 | 2 | k-means++ | 25 | 0.017 | NaN | 0.004 | 29 | 0.005 | 0.095 | 0.001 | 2.551 | 0.135 | See |
| 1 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | k-means++ | 25 | 0.009 | 0.0 | 0.004 | 29 | 0.005 | 0.001 | 0.000 | 2.650 | 0.639 | See |
| 2 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 2 | k-means++ | 25 | 0.013 | 0.0 | 0.004 | 29 | 0.005 | 0.000 | 0.000 | 10.093 | 7.601 | See |
| 3 | KMeans_short | fit | 0.107 | 0.000 | 10000 | 10000 | 2 | random | 30 | 0.045 | NaN | 0.004 | 30 | 0.005 | 0.041 | 0.001 | 2.628 | 0.035 | See |
| 4 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | random | 30 | 0.009 | 0.0 | 0.004 | 30 | 0.005 | 0.001 | 0.000 | 2.511 | 0.605 | See |
| 5 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 2 | random | 30 | 0.012 | 0.0 | 0.004 | 30 | 0.005 | 0.000 | 0.000 | 10.123 | 7.522 | See |
| 6 | KMeans_short | fit | 0.570 | 0.023 | 10000 | 10000 | 100 | k-means++ | 21 | 0.295 | NaN | 0.004 | 25 | 0.005 | 0.324 | 0.014 | 1.759 | 0.104 | See |
| 7 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 100 | k-means++ | 21 | 0.337 | 0.0 | 0.004 | 25 | 0.005 | 0.001 | 0.000 | 1.997 | 0.470 | See |
| 8 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 100 | k-means++ | 21 | 0.581 | 0.0 | 0.004 | 25 | 0.005 | 0.000 | 0.000 | 7.979 | 5.435 | See |
| 9 | KMeans_short | fit | 0.215 | 0.027 | 10000 | 10000 | 100 | random | 26 | 0.967 | NaN | 0.004 | 28 | 0.005 | 0.159 | 0.020 | 1.355 | 0.241 | See |
| 10 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 100 | random | 26 | 0.338 | 0.0 | 0.004 | 28 | 0.005 | 0.001 | 0.000 | 1.982 | 0.414 | See |
| 11 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 100 | random | 26 | 0.621 | 0.0 | 0.004 | 28 | 0.005 | 0.000 | 0.000 | 7.715 | 5.010 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | class_weight | l1_ratio | n_jobs | random_state | n_iter | throughput | latency | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 11.154 | 0.011 | 1000000 | 1000000 | 100 | NaN | NaN | NaN | NaN | [20] | [-0.10577568] | NaN | 0.28 | 11.214 | 0.125 | 0.995 | 0.011 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | NaN | NaN | [20] | 2.5380364415786185 | 0.0 | 0.28 | 0.000 | 0.000 | 0.838 | 0.534 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | NaN | NaN | [20] | 11.834144461224406 | 0.0 | 0.28 | 0.000 | 0.000 | 0.323 | 0.339 | See |
| 3 | LogisticRegression | fit | 0.756 | 0.015 | 1000 | 1000 | 10000 | NaN | NaN | NaN | NaN | [26] | [2.75221518] | NaN | 0.28 | 0.746 | 0.006 | 1.013 | 0.022 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | NaN | NaN | NaN | NaN | [26] | 4.875692691096868 | 0.0 | 0.28 | 0.003 | 0.001 | 0.561 | 0.154 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | NaN | NaN | [26] | 68.38682321937246 | 0.0 | 0.28 | 0.001 | 0.000 | 0.157 | 0.123 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | max_iter | random_state | throughput | latency | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 0.179 | 0.002 | 1000 | 1000 | 10000 | NaN | NaN | 0.447 | NaN | 1.0 | 0.186 | 0.001 | 0.961 | 0.013 | See |
| 1 | Ridge | predict | 0.012 | 0.000 | 1000 | 1000 | 10000 | NaN | NaN | 6.514 | 0.0 | 1.0 | 0.020 | 0.001 | 0.625 | 0.033 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | 1028.000 | 0.0 | 1.0 | 0.000 | 0.000 | 0.742 | 0.757 | See |
| 3 | Ridge | fit | 1.195 | 0.061 | 1000000 | 1000000 | 100 | NaN | NaN | 0.670 | NaN | 1.0 | 0.232 | 0.002 | 5.145 | 0.266 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | 5.080 | 0.0 | 1.0 | 0.000 | 0.000 | 0.670 | 0.603 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | 13.326 | 0.0 | 1.0 | 0.000 | 0.000 | 0.618 | 0.703 | See |
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